Computer Science

This course is a core course in the field of computer science. The focus of the course is to introduce students to the basic knowledge and algorithms in the field of artificial intelligence, and guide them to use artificial intelligence models to solve real-world problems in the era of big data through practical projects in the course.

Brief Introduction to the Course Content: The content of this course mainly covers several basic modules in the field of artificial intelligence, from simple to complex, gradually explaining the principles and techniques used by intelligent agents to solve real-world problems. Specific content includes: search algorithms (basic search algorithms for trees and graphs, etc.), Markov decision processes (Markov models, etc.), game algorithms (Alpha-Beta pruning techniques, etc.), uncertain information reasoning methods (Bayesian models), and knowledge representation methods (first-order, higher-order, and semantic representations, etc.). Through the combination of course practice, students will be provided with means and methods to solve real-world big data problems.

This course requires students to have the ability to write simple programs using Python or be able to quickly grasp the use of Python. Prerequisites for the course are computer data structures and foundations of probability theory. By studying this course, students will understand some core issues and applications in the field of artificial intelligence, and master the relevant principles and algorithms. In addition, students will acquire the ability to write, maintain, and test Python language, and be able to use Python to solve real-world problems.

This class provides a comprehensive overview of artificial intelligence and explores successful cases of problem-solving in various domains, attempting to solve real problems. This course explores how AI works in general, looks at different AI algorithms/models, and allows for practice building AI Models.

This course looks at the challenges and techniques involved in programming multicore systems. The course starts out with a brief history of computing to motivate the shift to multicore architectures. Parallelism, execution indeterminism, thread-and-lock-based programming, non-blocking synchronization, and HW acceleration with GPGPUs are introduced in a step-by-step approach that is accompanied by individual programming assignments. The impact of hardware architectures on programmability and performance is highlighted. Emerging trends such as Stream-parallel programming and hardware transactional memory are introduced. 

Mobile and pervasive intelligence enables diverse smart applications in our daily life. It provides new insights into unstructured and uncertain information from a variety of sensors, data sources, user devices, and mobile platforms. The lecture covers theoretical fundamentals in sensing, communications, computing, and autonomy techniques; how to apply them in practical systems, and design principles in mobile and pervasive applications. The content includes the following topics:

A: Sensation and perception of mobile platforms
Section 1-Sensing: Wireless, visual, acoustic, and privacy-preserving sensing techniques
Section 2-Communications: Advanced communication and networking technologies to connect hardware and software components in one or more pervasive systems.
 

B: Intelligence creation
Section 3-Computing: Context-aware computing, serverless computing, and distributed intelligence
Section 4-Autonomy: Autonomous coordination and collaboration techniques between mobile platforms (e.g., drones or robots)
 

C: Hands-on tutorials 

Teaching Objectives: Through the study of these contents, students will understand the basic concepts of multimedia technology, master the fundamental theories of multimedia technology, and the usage of commonly used multimedia tools. They will also gain knowledge on multimedia software development and multimedia production, laying a solid foundation for future research and development in the field of multimedia. Students will also learn about the current status and practical demands of multimedia technology, professional ethical requirements for multimedia practitioners, and ethical influences.

Overview: This course provides a comprehensive introduction to multimedia technology, including its definition and key characteristics, acquisition and processing of audio-video information, multimedia data compression and encoding techniques, and multimedia network communication technology.

This course examines the basic concepts of databases and the theories and techniques required to build a database application system. Topics include relational data model and query language, SQL language, database design theory, and application development methodology. 

This course introduces fundamental concepts for designing and implementing large‐scale distributed systems. The course not only focuses on the design aspects of distributed systems, but also on the fundamental principles to ensure the correctness in a distributed environment. Students apply the concepts via hands on assignments using GO programming language. The course also examines specific concepts of distributed systems e.g. designing distributed file systems (such as in Google File Systems) to accommodate arbitrarily many application‐level users. Finally, it discusses concepts on recovering from faults (both normal and byzantine) in a distributed system.

This course teaches the fundamental areas of computer graphics, including graphics hardware devices and mathematics. As an enrichment part of the course, it introduces the state-of-the-art development in computer graphics by viewing video clips and experimenting with demo program made available in the course web. Upon completion of the course, students understand the basic computer graphics terminology and concepts, and are able to design and implement simple 2D and 3D interactive computer graphics-related programs.

This course teaches basic knowledge of the programming language python, as well as data analysis skill via empirical application. 

This course aims to provide:

1. An understanding of the basic knowledge of the programming language python
2. An understanding of the basic machine learning methods, i.e., regression analysis.
3. An understanding of the data analysis process including data pre-processing, analysis, and the interpretation for the results based on real data.

This course provides an introduction to the design and implementation of modern general-purpose operating systems. It concentrates on the operating system kernel with the emphasis being on concepts that lead to practical implementations. Throughout the course reference is made to a number of widely-adopted general-purpose operating systems (including Linux, Windows, and OS X) to illustrate real implementations. An educational operating system is used for the coursework.